Overview

Dataset statistics

Number of variables38
Number of observations7043
Missing cells26972
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory304.0 B

Variable types

Categorical10
Numeric15
Boolean13

Alerts

Customer ID has a high cardinality: 7043 distinct values High cardinality
City has a high cardinality: 1106 distinct values High cardinality
Zip Code is highly correlated with Latitude and 1 other fieldsHigh correlation
Latitude is highly correlated with Zip Code and 2 other fieldsHigh correlation
Longitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Tenure in Months is highly correlated with Offer and 7 other fieldsHigh correlation
Avg Monthly Long Distance Charges is highly correlated with Total Long Distance ChargesHigh correlation
Monthly Charge is highly correlated with Phone Service and 7 other fieldsHigh correlation
Total Charges is highly correlated with Tenure in Months and 10 other fieldsHigh correlation
Total Long Distance Charges is highly correlated with Tenure in Months and 3 other fieldsHigh correlation
Total Revenue is highly correlated with Tenure in Months and 10 other fieldsHigh correlation
Age is highly correlated with Avg Monthly GB DownloadHigh correlation
Avg Monthly GB Download is highly correlated with AgeHigh correlation
Streaming Movies is highly correlated with Streaming TV and 4 other fieldsHigh correlation
Customer Status is highly correlated with Tenure in Months and 2 other fieldsHigh correlation
Internet Service is highly correlated with Monthly Charge and 1 other fieldsHigh correlation
Churn Category is highly correlated with Churn ReasonHigh correlation
Online Security is highly correlated with Internet ServiceHigh correlation
Internet Type is highly correlated with Monthly ChargeHigh correlation
Online Backup is highly correlated with Tenure in Months and 2 other fieldsHigh correlation
Unlimited Data is highly correlated with Total Extra Data ChargesHigh correlation
Streaming Music is highly correlated with Streaming TV and 1 other fieldsHigh correlation
Premium Tech Support is highly correlated with Internet ServiceHigh correlation
Multiple Lines is highly correlated with Monthly Charge and 2 other fieldsHigh correlation
Phone Service is highly correlated with Monthly ChargeHigh correlation
Churn Reason is highly correlated with Latitude and 1 other fieldsHigh correlation
Streaming TV is highly correlated with Streaming Movies and 4 other fieldsHigh correlation
Device Protection Plan is highly correlated with Tenure in Months and 2 other fieldsHigh correlation
Married is highly correlated with Number of ReferralsHigh correlation
Number of Referrals is highly correlated with MarriedHigh correlation
Offer is highly correlated with Tenure in Months and 4 other fieldsHigh correlation
Contract is highly correlated with Tenure in Months and 3 other fieldsHigh correlation
Total Extra Data Charges is highly correlated with Unlimited DataHigh correlation
Avg Monthly Long Distance Charges has 682 (9.7%) missing values Missing
Multiple Lines has 682 (9.7%) missing values Missing
Internet Type has 1526 (21.7%) missing values Missing
Avg Monthly GB Download has 1526 (21.7%) missing values Missing
Online Security has 1526 (21.7%) missing values Missing
Online Backup has 1526 (21.7%) missing values Missing
Device Protection Plan has 1526 (21.7%) missing values Missing
Premium Tech Support has 1526 (21.7%) missing values Missing
Streaming TV has 1526 (21.7%) missing values Missing
Streaming Movies has 1526 (21.7%) missing values Missing
Streaming Music has 1526 (21.7%) missing values Missing
Unlimited Data has 1526 (21.7%) missing values Missing
Churn Category has 5174 (73.5%) missing values Missing
Churn Reason has 5174 (73.5%) missing values Missing
Customer ID is uniformly distributed Uniform
Customer ID has unique values Unique
Number of Dependents has 5416 (76.9%) zeros Zeros
Number of Referrals has 3821 (54.3%) zeros Zeros
Total Refunds has 6518 (92.5%) zeros Zeros
Total Extra Data Charges has 6315 (89.7%) zeros Zeros
Total Long Distance Charges has 682 (9.7%) zeros Zeros

Reproduction

Analysis started2022-11-27 14:08:05.228891
Analysis finished2022-11-27 14:08:21.253795
Duration16.02 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Customer ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
0002-ORFBO
 
1
6616-AALSR
 
1
6625-UTXEW
 
1
6625-IUTTT
 
1
6625-FLENO
 
1
Other values (7038)
7038 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7043 ?
Unique (%)100.0%

Sample

1st row0002-ORFBO
2nd row0003-MKNFE
3rd row0004-TLHLJ
4th row0011-IGKFF
5th row0013-EXCHZ

Common Values

ValueCountFrequency (%)
0002-ORFBO1
 
< 0.1%
6616-AALSR1
 
< 0.1%
6625-UTXEW1
 
< 0.1%
6625-IUTTT1
 
< 0.1%
6625-FLENO1
 
< 0.1%
6624-JDRDS1
 
< 0.1%
6621-YOBKI1
 
< 0.1%
6621-NRZAK1
 
< 0.1%
6620-JDYNW1
 
< 0.1%
6620-HVDUJ1
 
< 0.1%
Other values (7033)7033
99.9%

Length

2022-11-27T19:38:21.285144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0002-orfbo1
 
< 0.1%
0019-efaep1
 
< 0.1%
0011-igkff1
 
< 0.1%
0013-exchz1
 
< 0.1%
0013-mhzwf1
 
< 0.1%
0013-smeoe1
 
< 0.1%
0014-bmaqu1
 
< 0.1%
0015-uocoj1
 
< 0.1%
0016-qljis1
 
< 0.1%
0017-dinoc1
 
< 0.1%
Other values (7033)7033
99.9%

Most occurring characters

ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter35215
50.0%
Decimal Number28172
40.0%
Dash Punctuation7043
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O1442
 
4.1%
H1396
 
4.0%
B1393
 
4.0%
S1386
 
3.9%
V1382
 
3.9%
T1374
 
3.9%
Z1368
 
3.9%
C1368
 
3.9%
F1363
 
3.9%
L1363
 
3.9%
Other values (16)21380
60.7%
Decimal Number
ValueCountFrequency (%)
22901
10.3%
92881
10.2%
62870
10.2%
72836
10.1%
02831
10.0%
82812
10.0%
52810
10.0%
32791
9.9%
12726
9.7%
42714
9.6%
Dash Punctuation
ValueCountFrequency (%)
-7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35215
50.0%
Latin35215
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O1442
 
4.1%
H1396
 
4.0%
B1393
 
4.0%
S1386
 
3.9%
V1382
 
3.9%
T1374
 
3.9%
Z1368
 
3.9%
C1368
 
3.9%
F1363
 
3.9%
L1363
 
3.9%
Other values (16)21380
60.7%
Common
ValueCountFrequency (%)
-7043
20.0%
22901
8.2%
92881
8.2%
62870
8.1%
72836
8.1%
02831
8.0%
82812
 
8.0%
52810
 
8.0%
32791
 
7.9%
12726
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII70430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Male
3555 
Female
3488 

Length

Max length6
Median length4
Mean length4.990487008
Min length4

Characters and Unicode

Total characters35148
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male3555
50.5%
Female3488
49.5%

Length

2022-11-27T19:38:21.329386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T19:38:21.374072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
male3555
50.5%
female3488
49.5%

Most occurring characters

ValueCountFrequency (%)
e10531
30.0%
a7043
20.0%
l7043
20.0%
M3555
 
10.1%
F3488
 
9.9%
m3488
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28105
80.0%
Uppercase Letter7043
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10531
37.5%
a7043
25.1%
l7043
25.1%
m3488
 
12.4%
Uppercase Letter
ValueCountFrequency (%)
M3555
50.5%
F3488
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin35148
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10531
30.0%
a7043
20.0%
l7043
20.0%
M3555
 
10.1%
F3488
 
9.9%
m3488
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII35148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10531
30.0%
a7043
20.0%
l7043
20.0%
M3555
 
10.1%
F3488
 
9.9%
m3488
 
9.9%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.50972597
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:21.419761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q132
median46
Q360
95-th percentile75
Maximum80
Range61
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.75035166
Coefficient of variation (CV)0.3601472876
Kurtosis-1.00284948
Mean46.50972597
Median Absolute Deviation (MAD)14
Skewness0.1621864487
Sum327568
Variance280.5742806
MonotonicityNot monotonic
2022-11-27T19:38:21.475973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42156
 
2.2%
47153
 
2.2%
40150
 
2.1%
44148
 
2.1%
23146
 
2.1%
56144
 
2.0%
62143
 
2.0%
35142
 
2.0%
21140
 
2.0%
33139
 
2.0%
Other values (52)5582
79.3%
ValueCountFrequency (%)
19127
1.8%
20127
1.8%
21140
2.0%
22130
1.8%
23146
2.1%
24109
1.5%
25138
2.0%
26115
1.6%
27132
1.9%
28119
1.7%
ValueCountFrequency (%)
8066
0.9%
7976
1.1%
7863
0.9%
7772
1.0%
7669
1.0%
7574
1.1%
7476
1.1%
7385
1.2%
7258
0.8%
7168
1.0%

Married
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3641 
True
3402 
ValueCountFrequency (%)
False3641
51.7%
True3402
48.3%
2022-11-27T19:38:21.527177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Number of Dependents
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4686923186
Minimum0
Maximum9
Zeros5416
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:21.561203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9628019509
Coefficient of variation (CV)2.054230276
Kurtosis4.446357934
Mean0.4686923186
Median Absolute Deviation (MAD)0
Skewness2.109931981
Sum3301
Variance0.9269875967
MonotonicityNot monotonic
2022-11-27T19:38:21.594818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
05416
76.9%
1553
 
7.9%
2531
 
7.5%
3517
 
7.3%
510
 
0.1%
49
 
0.1%
63
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
05416
76.9%
1553
 
7.9%
2531
 
7.5%
3517
 
7.3%
49
 
0.1%
510
 
0.1%
63
 
< 0.1%
72
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
81
 
< 0.1%
72
 
< 0.1%
63
 
< 0.1%
510
 
0.1%
49
 
0.1%
3517
 
7.3%
2531
 
7.5%
1553
 
7.9%
05416
76.9%

City
Categorical

HIGH CARDINALITY

Distinct1106
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Los Angeles
 
293
San Diego
 
285
San Jose
 
112
Sacramento
 
108
San Francisco
 
104
Other values (1101)
6141 

Length

Max length22
Median length19
Mean length9.203464433
Min length3

Characters and Unicode

Total characters64820
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrazier Park
2nd rowGlendale
3rd rowCosta Mesa
4th rowMartinez
5th rowCamarillo

Common Values

ValueCountFrequency (%)
Los Angeles293
 
4.2%
San Diego285
 
4.0%
San Jose112
 
1.6%
Sacramento108
 
1.5%
San Francisco104
 
1.5%
Fresno61
 
0.9%
Long Beach60
 
0.9%
Oakland52
 
0.7%
Escondido51
 
0.7%
Stockton44
 
0.6%
Other values (1096)5873
83.4%

Length

2022-11-27T19:38:21.641494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san718
 
6.9%
los337
 
3.3%
angeles293
 
2.8%
diego285
 
2.8%
santa181
 
1.8%
valley171
 
1.7%
beach169
 
1.6%
city150
 
1.5%
sacramento116
 
1.1%
jose112
 
1.1%
Other values (1110)7807
75.5%

Most occurring characters

ValueCountFrequency (%)
a6946
 
10.7%
e6111
 
9.4%
n5134
 
7.9%
o5074
 
7.8%
l3970
 
6.1%
r3568
 
5.5%
i3423
 
5.3%
3296
 
5.1%
s2853
 
4.4%
t2602
 
4.0%
Other values (42)21843
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51185
79.0%
Uppercase Letter10339
 
16.0%
Space Separator3296
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6946
13.6%
e6111
11.9%
n5134
10.0%
o5074
9.9%
l3970
7.8%
r3568
 
7.0%
i3423
 
6.7%
s2853
 
5.6%
t2602
 
5.1%
d1669
 
3.3%
Other values (16)9835
19.2%
Uppercase Letter
ValueCountFrequency (%)
S1576
15.2%
C977
 
9.4%
L869
 
8.4%
B731
 
7.1%
A651
 
6.3%
M599
 
5.8%
P582
 
5.6%
D533
 
5.2%
F471
 
4.6%
R447
 
4.3%
Other values (15)2903
28.1%
Space Separator
ValueCountFrequency (%)
3296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61524
94.9%
Common3296
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6946
 
11.3%
e6111
 
9.9%
n5134
 
8.3%
o5074
 
8.2%
l3970
 
6.5%
r3568
 
5.8%
i3423
 
5.6%
s2853
 
4.6%
t2602
 
4.2%
d1669
 
2.7%
Other values (41)20174
32.8%
Common
ValueCountFrequency (%)
3296
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII64820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a6946
 
10.7%
e6111
 
9.4%
n5134
 
7.9%
o5074
 
7.8%
l3970
 
6.1%
r3568
 
5.5%
i3423
 
5.3%
3296
 
5.1%
s2853
 
4.4%
t2602
 
4.0%
Other values (42)21843
33.7%

Zip Code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93486.07057
Minimum90001
Maximum96150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:21.699445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90241.1
Q192101
median93518
Q395329
95-th percentile96020.9
Maximum96150
Range6149
Interquartile range (IQR)3228

Descriptive statistics

Standard deviation1856.767505
Coefficient of variation (CV)0.01986143491
Kurtosis-1.173915431
Mean93486.07057
Median Absolute Deviation (MAD)1605
Skewness-0.2096151191
Sum658422395
Variance3447585.568
MonotonicityNot monotonic
2022-11-27T19:38:21.755682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9202843
 
0.6%
9202738
 
0.5%
9212236
 
0.5%
9211734
 
0.5%
9212632
 
0.5%
9259230
 
0.4%
9210927
 
0.4%
9213022
 
0.3%
9212120
 
0.3%
9212916
 
0.2%
Other values (1616)6745
95.8%
ValueCountFrequency (%)
900014
0.1%
900024
0.1%
900035
0.1%
900045
0.1%
900054
0.1%
900065
0.1%
900075
0.1%
900085
0.1%
900104
0.1%
900115
0.1%
ValueCountFrequency (%)
961502
< 0.1%
961484
0.1%
961464
0.1%
961453
< 0.1%
961434
0.1%
961423
< 0.1%
961413
< 0.1%
961404
0.1%
961374
0.1%
961364
0.1%

Latitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.19745482
Minimum32.555828
Maximum41.962127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:21.813498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum32.555828
5-th percentile32.886925
Q133.990646
median36.205465
Q338.161321
95-th percentile40.4974252
Maximum41.962127
Range9.406299
Interquartile range (IQR)4.170675

Descriptive statistics

Standard deviation2.468928682
Coefficient of variation (CV)0.06820724534
Kurtosis-1.160506077
Mean36.19745482
Median Absolute Deviation (MAD)2.169863
Skewness0.3148042686
Sum254938.6743
Variance6.095608835
MonotonicityNot monotonic
2022-11-27T19:38:21.869923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.36257543
 
0.6%
33.14126538
 
0.5%
32.8572336
 
0.5%
32.82508634
 
0.5%
32.88692532
 
0.5%
33.50725530
 
0.4%
32.78783627
 
0.4%
32.95719522
 
0.3%
32.89861320
 
0.3%
32.96106416
 
0.2%
Other values (1616)6745
95.8%
ValueCountFrequency (%)
32.5558285
0.1%
32.5781034
0.1%
32.5791344
0.1%
32.5875575
0.1%
32.6050124
0.1%
32.6079645
0.1%
32.6194655
0.1%
32.6229994
0.1%
32.6367924
0.1%
32.641645
0.1%
ValueCountFrequency (%)
41.9621274
0.1%
41.9506834
0.1%
41.9492164
0.1%
41.9322073
< 0.1%
41.9241743
< 0.1%
41.8679084
0.1%
41.8319014
0.1%
41.8165954
0.1%
41.8135214
0.1%
41.7697094
0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION

Distinct1625
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.7566837
Minimum-124.301372
Maximum-114.192901
Zeros0
Zeros (%)0.0%
Negative7043
Negative (%)100.0%
Memory size55.1 KiB
2022-11-27T19:38:21.925110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-124.301372
5-th percentile-122.9755
Q1-121.78809
median-119.595293
Q3-117.969795
95-th percentile-116.873258
Maximum-114.192901
Range10.108471
Interquartile range (IQR)3.818295

Descriptive statistics

Standard deviation2.154425092
Coefficient of variation (CV)-0.01799001965
Kurtosis-1.191290554
Mean-119.7566837
Median Absolute Deviation (MAD)1.848851
Skewness-0.09193163502
Sum-843446.3231
Variance4.641547478
MonotonicityNot monotonic
2022-11-27T19:38:21.977946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-117.29964443
 
0.6%
-116.96722138
 
0.5%
-117.20977436
 
0.5%
-117.19942434
 
0.5%
-117.15216232
 
0.5%
-117.02947330
 
0.4%
-117.23237627
 
0.4%
-117.20254222
 
0.3%
-117.20293720
 
0.3%
-117.13491716
 
0.2%
Other values (1615)6745
95.8%
ValueCountFrequency (%)
-124.3013724
0.1%
-124.2400514
0.1%
-124.2173784
0.1%
-124.2109024
0.1%
-124.1899774
0.1%
-124.1632344
0.1%
-124.154284
0.1%
-124.1215044
0.1%
-124.1088974
0.1%
-124.0987394
0.1%
ValueCountFrequency (%)
-114.1929014
0.1%
-114.365145
0.1%
-114.7022564
0.1%
-114.716124
0.1%
-114.7583345
0.1%
-114.8507844
0.1%
-115.1528652
 
< 0.1%
-115.1918575
0.1%
-115.2570095
0.1%
-115.2879014
0.1%

Number of Referrals
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.951867102
Minimum0
Maximum11
Zeros3821
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:22.023364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0011993
Coefficient of variation (CV)1.537604326
Kurtosis0.7219639341
Mean1.951867102
Median Absolute Deviation (MAD)0
Skewness1.446059625
Sum13747
Variance9.007197238
MonotonicityNot monotonic
2022-11-27T19:38:22.059122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
03821
54.3%
11086
 
15.4%
5264
 
3.7%
3255
 
3.6%
7248
 
3.5%
9238
 
3.4%
2236
 
3.4%
4236
 
3.4%
10223
 
3.2%
6221
 
3.1%
Other values (2)215
 
3.1%
ValueCountFrequency (%)
03821
54.3%
11086
 
15.4%
2236
 
3.4%
3255
 
3.6%
4236
 
3.4%
5264
 
3.7%
6221
 
3.1%
7248
 
3.5%
8213
 
3.0%
9238
 
3.4%
ValueCountFrequency (%)
112
 
< 0.1%
10223
3.2%
9238
3.4%
8213
3.0%
7248
3.5%
6221
3.1%
5264
3.7%
4236
3.4%
3255
3.6%
2236
3.4%

Tenure in Months
Real number (ℝ≥0)

HIGH CORRELATION

Distinct72
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.386767
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:22.105813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.54206101
Coefficient of variation (CV)0.757780516
Kurtosis-1.387052361
Mean32.386767
Median Absolute Deviation (MAD)22
Skewness0.2405426141
Sum228100
Variance602.3127587
MonotonicityNot monotonic
2022-11-27T19:38:22.156108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1613
 
8.7%
72362
 
5.1%
2238
 
3.4%
3200
 
2.8%
4176
 
2.5%
71170
 
2.4%
5133
 
1.9%
7131
 
1.9%
10127
 
1.8%
8123
 
1.7%
Other values (62)4770
67.7%
ValueCountFrequency (%)
1613
8.7%
2238
 
3.4%
3200
 
2.8%
4176
 
2.5%
5133
 
1.9%
6110
 
1.6%
7131
 
1.9%
8123
 
1.7%
9119
 
1.7%
10127
 
1.8%
ValueCountFrequency (%)
72362
5.1%
71170
2.4%
70119
 
1.7%
6995
 
1.3%
68100
 
1.4%
6798
 
1.4%
6689
 
1.3%
6576
 
1.1%
6480
 
1.1%
6372
 
1.0%

Offer
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
None
3877 
Offer B
824 
Offer E
805 
Offer D
602 
Offer A
520 

Length

Max length7
Median length4
Mean length5.348573051
Min length4

Characters and Unicode

Total characters37670
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowOffer E
4th rowOffer D
5th rowNone

Common Values

ValueCountFrequency (%)
None3877
55.0%
Offer B824
 
11.7%
Offer E805
 
11.4%
Offer D602
 
8.5%
Offer A520
 
7.4%
Offer C415
 
5.9%

Length

2022-11-27T19:38:22.207089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T19:38:22.258732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
none3877
38.0%
offer3166
31.0%
b824
 
8.1%
e805
 
7.9%
d602
 
5.9%
a520
 
5.1%
c415
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e7043
18.7%
f6332
16.8%
N3877
10.3%
o3877
10.3%
n3877
10.3%
O3166
8.4%
r3166
8.4%
3166
8.4%
B824
 
2.2%
E805
 
2.1%
Other values (3)1537
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24295
64.5%
Uppercase Letter10209
27.1%
Space Separator3166
 
8.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N3877
38.0%
O3166
31.0%
B824
 
8.1%
E805
 
7.9%
D602
 
5.9%
A520
 
5.1%
C415
 
4.1%
Lowercase Letter
ValueCountFrequency (%)
e7043
29.0%
f6332
26.1%
o3877
16.0%
n3877
16.0%
r3166
13.0%
Space Separator
ValueCountFrequency (%)
3166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin34504
91.6%
Common3166
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e7043
20.4%
f6332
18.4%
N3877
11.2%
o3877
11.2%
n3877
11.2%
O3166
9.2%
r3166
9.2%
B824
 
2.4%
E805
 
2.3%
D602
 
1.7%
Other values (2)935
 
2.7%
Common
ValueCountFrequency (%)
3166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII37670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e7043
18.7%
f6332
16.8%
N3877
10.3%
o3877
10.3%
n3877
10.3%
O3166
8.4%
r3166
8.4%
3166
8.4%
B824
 
2.2%
E805
 
2.1%
Other values (3)1537
 
4.1%

Phone Service
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True6361
90.3%
False682
 
9.7%
2022-11-27T19:38:22.329983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Avg Monthly Long Distance Charges
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct3583
Distinct (%)56.3%
Missing682
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean25.42051721
Minimum1.01
Maximum49.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:22.372767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile3.27
Q113.05
median25.69
Q337.68
95-th percentile47.64
Maximum49.99
Range48.98
Interquartile range (IQR)24.63

Descriptive statistics

Standard deviation14.20037358
Coefficient of variation (CV)0.5586185938
Kurtosis-1.20599849
Mean25.42051721
Median Absolute Deviation (MAD)12.29
Skewness-0.001970967867
Sum161699.91
Variance201.6506098
MonotonicityNot monotonic
2022-11-27T19:38:22.427005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.267
 
0.1%
30.076
 
0.1%
45.926
 
0.1%
30.096
 
0.1%
42.556
 
0.1%
49.516
 
0.1%
22.566
 
0.1%
22.836
 
0.1%
41.936
 
0.1%
18.426
 
0.1%
Other values (3573)6300
89.5%
(Missing)682
 
9.7%
ValueCountFrequency (%)
1.011
 
< 0.1%
1.023
< 0.1%
1.031
 
< 0.1%
1.051
 
< 0.1%
1.061
 
< 0.1%
1.071
 
< 0.1%
1.082
< 0.1%
1.092
< 0.1%
1.11
 
< 0.1%
1.123
< 0.1%
ValueCountFrequency (%)
49.991
 
< 0.1%
49.983
< 0.1%
49.962
< 0.1%
49.952
< 0.1%
49.941
 
< 0.1%
49.921
 
< 0.1%
49.913
< 0.1%
49.93
< 0.1%
49.881
 
< 0.1%
49.871
 
< 0.1%

Multiple Lines
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing682
Missing (%)9.7%
Memory size13.9 KiB
False
3390 
True
2971 
(Missing)
682 
ValueCountFrequency (%)
False3390
48.1%
True2971
42.2%
(Missing)682
 
9.7%
2022-11-27T19:38:22.474483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Internet Service
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
5517 
False
1526 
ValueCountFrequency (%)
True5517
78.3%
False1526
 
21.7%
2022-11-27T19:38:22.510923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Internet Type
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing1526
Missing (%)21.7%
Memory size55.1 KiB
Fiber Optic
3035 
DSL
1652 
Cable
830 

Length

Max length11
Median length11
Mean length7.701830705
Min length3

Characters and Unicode

Total characters42491
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCable
2nd rowCable
3rd rowFiber Optic
4th rowFiber Optic
5th rowFiber Optic

Common Values

ValueCountFrequency (%)
Fiber Optic3035
43.1%
DSL1652
23.5%
Cable830
 
11.8%
(Missing)1526
21.7%

Length

2022-11-27T19:38:22.558887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T19:38:22.601559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
fiber3035
35.5%
optic3035
35.5%
dsl1652
19.3%
cable830
 
9.7%

Most occurring characters

ValueCountFrequency (%)
i6070
14.3%
b3865
9.1%
e3865
9.1%
F3035
7.1%
r3035
7.1%
3035
7.1%
O3035
7.1%
p3035
7.1%
t3035
7.1%
c3035
7.1%
Other values (6)7446
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter27600
65.0%
Uppercase Letter11856
27.9%
Space Separator3035
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i6070
22.0%
b3865
14.0%
e3865
14.0%
r3035
11.0%
p3035
11.0%
t3035
11.0%
c3035
11.0%
a830
 
3.0%
l830
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
F3035
25.6%
O3035
25.6%
D1652
13.9%
S1652
13.9%
L1652
13.9%
C830
 
7.0%
Space Separator
ValueCountFrequency (%)
3035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39456
92.9%
Common3035
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i6070
15.4%
b3865
9.8%
e3865
9.8%
F3035
7.7%
r3035
7.7%
O3035
7.7%
p3035
7.7%
t3035
7.7%
c3035
7.7%
D1652
 
4.2%
Other values (5)5794
14.7%
Common
ValueCountFrequency (%)
3035
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i6070
14.3%
b3865
9.1%
e3865
9.1%
F3035
7.1%
r3035
7.1%
3035
7.1%
O3035
7.1%
p3035
7.1%
t3035
7.1%
c3035
7.1%
Other values (6)7446
17.5%

Avg Monthly GB Download
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct49
Distinct (%)0.9%
Missing1526
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean26.18995831
Minimum2
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:22.644804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q113
median21
Q330
95-th percentile71
Maximum85
Range83
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.58658527
Coefficient of variation (CV)0.7478662257
Kurtosis0.6368415438
Mean26.18995831
Median Absolute Deviation (MAD)9
Skewness1.184055978
Sum144490
Variance383.6343226
MonotonicityNot monotonic
2022-11-27T19:38:22.696311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
19220
 
3.1%
27199
 
2.8%
30193
 
2.7%
59192
 
2.7%
26191
 
2.7%
23179
 
2.5%
22172
 
2.4%
21171
 
2.4%
18164
 
2.3%
13164
 
2.3%
Other values (39)3672
52.1%
(Missing)1526
21.7%
ValueCountFrequency (%)
2116
1.6%
3130
1.8%
4129
1.8%
5114
1.6%
6114
1.6%
7116
1.6%
8120
1.7%
9116
1.6%
10132
1.9%
11145
2.1%
ValueCountFrequency (%)
8548
 
0.7%
8243
 
0.6%
7658
 
0.8%
7515
 
0.2%
7381
1.2%
7142
 
0.6%
6975
 
1.1%
59192
2.7%
5845
 
0.6%
5734
 
0.5%

Online Security
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3498 
True
2019 
(Missing)
1526 
ValueCountFrequency (%)
False3498
49.7%
True2019
28.7%
(Missing)1526
21.7%
2022-11-27T19:38:22.743026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Online Backup
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3088 
True
2429 
(Missing)
1526 
ValueCountFrequency (%)
False3088
43.8%
True2429
34.5%
(Missing)1526
21.7%
2022-11-27T19:38:22.777674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Device Protection Plan
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3095 
True
2422 
(Missing)
1526 
ValueCountFrequency (%)
False3095
43.9%
True2422
34.4%
(Missing)1526
21.7%
2022-11-27T19:38:22.812188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Premium Tech Support
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3473 
True
2044 
(Missing)
1526 
ValueCountFrequency (%)
False3473
49.3%
True2044
29.0%
(Missing)1526
21.7%
2022-11-27T19:38:22.846337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Streaming TV
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
2810 
True
2707 
(Missing)
1526 
ValueCountFrequency (%)
False2810
39.9%
True2707
38.4%
(Missing)1526
21.7%
2022-11-27T19:38:22.882157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Streaming Movies
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
2785 
True
2732 
(Missing)
1526 
ValueCountFrequency (%)
False2785
39.5%
True2732
38.8%
(Missing)1526
21.7%
2022-11-27T19:38:22.917093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Streaming Music
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
False
3029 
True
2488 
(Missing)
1526 
ValueCountFrequency (%)
False3029
43.0%
True2488
35.3%
(Missing)1526
21.7%
2022-11-27T19:38:22.951975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Unlimited Data
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1526
Missing (%)21.7%
Memory size13.9 KiB
True
4745 
False
772 
(Missing)
1526 
ValueCountFrequency (%)
True4745
67.4%
False772
 
11.0%
(Missing)1526
 
21.7%
2022-11-27T19:38:22.991729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Contract
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Month-to-Month
3610 
Two Year
1883 
One Year
1550 

Length

Max length14
Median length14
Mean length11.07539401
Min length8

Characters and Unicode

Total characters78004
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOne Year
2nd rowMonth-to-Month
3rd rowMonth-to-Month
4th rowMonth-to-Month
5th rowMonth-to-Month

Common Values

ValueCountFrequency (%)
Month-to-Month3610
51.3%
Two Year1883
26.7%
One Year1550
22.0%

Length

2022-11-27T19:38:23.029560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T19:38:23.073105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month3610
34.5%
year3433
32.8%
two1883
18.0%
one1550
14.8%

Most occurring characters

ValueCountFrequency (%)
o12713
16.3%
t10830
13.9%
n8770
11.2%
M7220
9.3%
h7220
9.3%
-7220
9.3%
e4983
 
6.4%
3433
 
4.4%
Y3433
 
4.4%
a3433
 
4.4%
Other values (4)8749
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter53265
68.3%
Uppercase Letter14086
 
18.1%
Dash Punctuation7220
 
9.3%
Space Separator3433
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o12713
23.9%
t10830
20.3%
n8770
16.5%
h7220
13.6%
e4983
 
9.4%
a3433
 
6.4%
r3433
 
6.4%
w1883
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
M7220
51.3%
Y3433
24.4%
T1883
 
13.4%
O1550
 
11.0%
Dash Punctuation
ValueCountFrequency (%)
-7220
100.0%
Space Separator
ValueCountFrequency (%)
3433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin67351
86.3%
Common10653
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o12713
18.9%
t10830
16.1%
n8770
13.0%
M7220
10.7%
h7220
10.7%
e4983
 
7.4%
Y3433
 
5.1%
a3433
 
5.1%
r3433
 
5.1%
T1883
 
2.8%
Other values (2)3433
 
5.1%
Common
ValueCountFrequency (%)
-7220
67.8%
3433
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII78004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o12713
16.3%
t10830
13.9%
n8770
11.2%
M7220
9.3%
h7220
9.3%
-7220
9.3%
e4983
 
6.4%
3433
 
4.4%
Y3433
 
4.4%
a3433
 
4.4%
Other values (4)8749
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True4171
59.2%
False2872
40.8%
2022-11-27T19:38:23.113450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Payment Method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Bank Withdrawal
3909 
Credit Card
2749 
Mailed Check
 
385

Length

Max length15
Median length15
Mean length13.27474088
Min length11

Characters and Unicode

Total characters93494
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCredit Card
3rd rowBank Withdrawal
4th rowBank Withdrawal
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Bank Withdrawal3909
55.5%
Credit Card2749
39.0%
Mailed Check385
 
5.5%

Length

2022-11-27T19:38:23.152013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T19:38:23.197223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
bank3909
27.8%
withdrawal3909
27.8%
credit2749
19.5%
card2749
19.5%
mailed385
 
2.7%
check385
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a14861
15.9%
d9792
10.5%
r9407
10.1%
7043
 
7.5%
i7043
 
7.5%
t6658
 
7.1%
C5883
 
6.3%
h4294
 
4.6%
k4294
 
4.6%
l4294
 
4.6%
Other values (7)19925
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter72365
77.4%
Uppercase Letter14086
 
15.1%
Space Separator7043
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a14861
20.5%
d9792
13.5%
r9407
13.0%
i7043
9.7%
t6658
9.2%
h4294
 
5.9%
k4294
 
5.9%
l4294
 
5.9%
w3909
 
5.4%
n3909
 
5.4%
Other values (2)3904
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
C5883
41.8%
B3909
27.8%
W3909
27.8%
M385
 
2.7%
Space Separator
ValueCountFrequency (%)
7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin86451
92.5%
Common7043
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a14861
17.2%
d9792
11.3%
r9407
10.9%
i7043
8.1%
t6658
7.7%
C5883
 
6.8%
h4294
 
5.0%
k4294
 
5.0%
l4294
 
5.0%
w3909
 
4.5%
Other values (6)16016
18.5%
Common
ValueCountFrequency (%)
7043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII93494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a14861
15.9%
d9792
10.5%
r9407
10.1%
7043
 
7.5%
i7043
 
7.5%
t6658
 
7.1%
C5883
 
6.3%
h4294
 
4.6%
k4294
 
4.6%
l4294
 
4.6%
Other values (7)19925
21.3%

Monthly Charge
Real number (ℝ)

HIGH CORRELATION

Distinct1591
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.59613091
Minimum-10
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative120
Negative (%)1.7%
Memory size55.1 KiB
2022-11-27T19:38:23.240583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile19.5
Q130.4
median70.05
Q389.75
95-th percentile107.195
Maximum118.75
Range128.75
Interquartile range (IQR)59.35

Descriptive statistics

Standard deviation31.20474312
Coefficient of variation (CV)0.4906704648
Kurtosis-1.125789607
Mean63.59613091
Median Absolute Deviation (MAD)24.5
Skewness-0.2753938343
Sum447907.55
Variance973.7359929
MonotonicityNot monotonic
2022-11-27T19:38:23.292772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0560
 
0.9%
19.8545
 
0.6%
19.9544
 
0.6%
19.944
 
0.6%
2043
 
0.6%
19.6542
 
0.6%
19.740
 
0.6%
20.2539
 
0.6%
20.1539
 
0.6%
19.5539
 
0.6%
Other values (1581)6608
93.8%
ValueCountFrequency (%)
-1013
0.2%
-99
0.1%
-812
0.2%
-716
0.2%
-66
 
0.1%
-511
0.2%
-417
0.2%
-313
0.2%
-210
0.1%
-113
0.2%
ValueCountFrequency (%)
118.751
< 0.1%
118.651
< 0.1%
118.62
< 0.1%
118.351
< 0.1%
118.21
< 0.1%
117.81
< 0.1%
117.61
< 0.1%
117.51
< 0.1%
117.451
< 0.1%
117.351
< 0.1%

Total Charges
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6540
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2280.381264
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:23.343984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.65
Q1400.15
median1394.55
Q33786.6
95-th percentile6921.025
Maximum8684.8
Range8666
Interquartile range (IQR)3386.45

Descriptive statistics

Standard deviation2266.220462
Coefficient of variation (CV)0.9937901605
Kurtosis-0.2276926647
Mean2280.381264
Median Absolute Deviation (MAD)1219.75
Skewness0.9637910861
Sum16060725.24
Variance5135755.182
MonotonicityNot monotonic
2022-11-27T19:38:23.392054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.211
 
0.2%
19.759
 
0.1%
19.98
 
0.1%
20.058
 
0.1%
19.658
 
0.1%
19.557
 
0.1%
45.37
 
0.1%
20.156
 
0.1%
19.456
 
0.1%
20.256
 
0.1%
Other values (6530)6967
98.9%
ValueCountFrequency (%)
18.81
 
< 0.1%
18.852
< 0.1%
18.91
 
< 0.1%
191
 
< 0.1%
19.051
 
< 0.1%
19.13
< 0.1%
19.151
 
< 0.1%
19.24
0.1%
19.253
< 0.1%
19.34
0.1%
ValueCountFrequency (%)
8684.81
< 0.1%
8672.451
< 0.1%
8670.11
< 0.1%
8594.41
< 0.1%
8564.751
< 0.1%
8547.151
< 0.1%
8543.251
< 0.1%
8529.51
< 0.1%
8496.71
< 0.1%
8477.71
< 0.1%

Total Refunds
Real number (ℝ≥0)

ZEROS

Distinct500
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.962182309
Minimum0
Maximum49.79
Zeros6518
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:23.440161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18.149
Maximum49.79
Range49.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.902614384
Coefficient of variation (CV)4.027461847
Kurtosis18.35065824
Mean1.962182309
Median Absolute Deviation (MAD)0
Skewness4.328516701
Sum13819.65
Variance62.45131411
MonotonicityNot monotonic
2022-11-27T19:38:23.489697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06518
92.5%
16.562
 
< 0.1%
8.742
 
< 0.1%
1.312
 
< 0.1%
41.742
 
< 0.1%
25.672
 
< 0.1%
29.762
 
< 0.1%
18.552
 
< 0.1%
15.412
 
< 0.1%
27.62
 
< 0.1%
Other values (490)507
 
7.2%
ValueCountFrequency (%)
06518
92.5%
1.011
 
< 0.1%
1.091
 
< 0.1%
1.271
 
< 0.1%
1.312
 
< 0.1%
1.481
 
< 0.1%
1.651
 
< 0.1%
1.661
 
< 0.1%
1.691
 
< 0.1%
1.831
 
< 0.1%
ValueCountFrequency (%)
49.791
< 0.1%
49.761
< 0.1%
49.572
< 0.1%
49.531
< 0.1%
49.511
< 0.1%
49.381
< 0.1%
49.371
< 0.1%
49.241
< 0.1%
49.231
< 0.1%
49.221
< 0.1%

Total Extra Data Charges
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.860712764
Minimum0
Maximum150
Zeros6315
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:23.531662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile60
Maximum150
Range150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.104978
Coefficient of variation (CV)3.659237584
Kurtosis16.45887408
Mean6.860712764
Median Absolute Deviation (MAD)0
Skewness4.091209239
Sum48320
Variance630.2599204
MonotonicityNot monotonic
2022-11-27T19:38:23.566981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
06315
89.7%
10138
 
2.0%
4062
 
0.9%
3058
 
0.8%
2051
 
0.7%
8047
 
0.7%
10044
 
0.6%
5043
 
0.6%
15042
 
0.6%
13040
 
0.6%
Other values (6)203
 
2.9%
ValueCountFrequency (%)
06315
89.7%
10138
 
2.0%
2051
 
0.7%
3058
 
0.8%
4062
 
0.9%
5043
 
0.6%
6036
 
0.5%
7034
 
0.5%
8047
 
0.7%
9035
 
0.5%
ValueCountFrequency (%)
15042
0.6%
14038
0.5%
13040
0.6%
12028
0.4%
11032
0.5%
10044
0.6%
9035
0.5%
8047
0.7%
7034
0.5%
6036
0.5%

Total Long Distance Charges
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6068
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.0992617
Minimum0
Maximum3564.72
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:23.612474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.545
median401.44
Q31191.1
95-th percentile2577.877
Maximum3564.72
Range3564.72
Interquartile range (IQR)1120.555

Descriptive statistics

Standard deviation846.6600548
Coefficient of variation (CV)1.130237471
Kurtosis0.644092077
Mean749.0992617
Median Absolute Deviation (MAD)382.12
Skewness1.238281984
Sum5275906.1
Variance716833.2484
MonotonicityNot monotonic
2022-11-27T19:38:23.661111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0682
 
9.7%
15.64
 
0.1%
48.964
 
0.1%
22.864
 
0.1%
597.63
 
< 0.1%
2077.923
 
< 0.1%
1983
 
< 0.1%
15.283
 
< 0.1%
808.083
 
< 0.1%
41.13
 
< 0.1%
Other values (6058)6331
89.9%
ValueCountFrequency (%)
0682
9.7%
1.131
 
< 0.1%
1.151
 
< 0.1%
1.171
 
< 0.1%
1.231
 
< 0.1%
1.281
 
< 0.1%
1.471
 
< 0.1%
1.481
 
< 0.1%
1.51
 
< 0.1%
1.591
 
< 0.1%
ValueCountFrequency (%)
3564.721
< 0.1%
35641
< 0.1%
3536.641
< 0.1%
3515.921
< 0.1%
3508.821
< 0.1%
3501.721
< 0.1%
3493.441
< 0.1%
3492.721
< 0.1%
3487.681
< 0.1%
3482.641
< 0.1%

Total Revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6975
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3034.379056
Minimum21.36
Maximum11979.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-11-27T19:38:23.713001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.36
5-th percentile78.452
Q1605.61
median2108.64
Q34801.145
95-th percentile8747.041
Maximum11979.34
Range11957.98
Interquartile range (IQR)4195.535

Descriptive statistics

Standard deviation2865.204542
Coefficient of variation (CV)0.9442474025
Kurtosis-0.2034573897
Mean3034.379056
Median Absolute Deviation (MAD)1767.61
Skewness0.919410268
Sum21371131.69
Variance8209397.065
MonotonicityNot monotonic
2022-11-27T19:38:23.764334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.83
 
< 0.1%
116.273
 
< 0.1%
68.413
 
< 0.1%
66.563
 
< 0.1%
3386.42
 
< 0.1%
174.932
 
< 0.1%
608.852
 
< 0.1%
712.852
 
< 0.1%
3423.52
 
< 0.1%
88.752
 
< 0.1%
Other values (6965)7019
99.7%
ValueCountFrequency (%)
21.361
< 0.1%
21.41
< 0.1%
21.611
< 0.1%
22.081
< 0.1%
22.121
< 0.1%
22.251
< 0.1%
22.281
< 0.1%
22.541
< 0.1%
23.242
< 0.1%
23.281
< 0.1%
ValueCountFrequency (%)
11979.341
< 0.1%
11868.341
< 0.1%
11795.781
< 0.1%
11688.91
< 0.1%
11634.531
< 0.1%
11596.991
< 0.1%
11564.371
< 0.1%
11529.541
< 0.1%
11514.811
< 0.1%
11501.821
< 0.1%

Customer Status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Stayed
4720 
Churned
1869 
Joined
 
454

Length

Max length7
Median length6
Mean length6.265369871
Min length6

Characters and Unicode

Total characters44127
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStayed
2nd rowStayed
3rd rowChurned
4th rowChurned
5th rowChurned

Common Values

ValueCountFrequency (%)
Stayed4720
67.0%
Churned1869
 
26.5%
Joined454
 
6.4%

Length

2022-11-27T19:38:23.809579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T19:38:23.849754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
stayed4720
67.0%
churned1869
 
26.5%
joined454
 
6.4%

Most occurring characters

ValueCountFrequency (%)
e7043
16.0%
d7043
16.0%
S4720
10.7%
t4720
10.7%
a4720
10.7%
y4720
10.7%
n2323
 
5.3%
C1869
 
4.2%
h1869
 
4.2%
u1869
 
4.2%
Other values (4)3231
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37084
84.0%
Uppercase Letter7043
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e7043
19.0%
d7043
19.0%
t4720
12.7%
a4720
12.7%
y4720
12.7%
n2323
 
6.3%
h1869
 
5.0%
u1869
 
5.0%
r1869
 
5.0%
o454
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
S4720
67.0%
C1869
 
26.5%
J454
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin44127
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e7043
16.0%
d7043
16.0%
S4720
10.7%
t4720
10.7%
a4720
10.7%
y4720
10.7%
n2323
 
5.3%
C1869
 
4.2%
h1869
 
4.2%
u1869
 
4.2%
Other values (4)3231
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII44127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e7043
16.0%
d7043
16.0%
S4720
10.7%
t4720
10.7%
a4720
10.7%
y4720
10.7%
n2323
 
5.3%
C1869
 
4.2%
h1869
 
4.2%
u1869
 
4.2%
Other values (4)3231
7.3%

Churn Category
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.3%
Missing5174
Missing (%)73.5%
Memory size55.1 KiB
Competitor
841 
Dissatisfaction
321 
Attitude
314 
Price
211 
Other
182 

Length

Max length15
Median length10
Mean length9.471375067
Min length5

Characters and Unicode

Total characters17702
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor
2nd rowDissatisfaction
3rd rowDissatisfaction
4th rowDissatisfaction
5th rowCompetitor

Common Values

ValueCountFrequency (%)
Competitor841
 
11.9%
Dissatisfaction321
 
4.6%
Attitude314
 
4.5%
Price211
 
3.0%
Other182
 
2.6%
(Missing)5174
73.5%

Length

2022-11-27T19:38:23.886556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-27T19:38:23.929875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
competitor841
45.0%
dissatisfaction321
 
17.2%
attitude314
 
16.8%
price211
 
11.3%
other182
 
9.7%

Most occurring characters

ValueCountFrequency (%)
t3448
19.5%
i2329
13.2%
o2003
11.3%
e1548
8.7%
r1234
 
7.0%
s963
 
5.4%
C841
 
4.8%
m841
 
4.8%
p841
 
4.8%
a642
 
3.6%
Other values (10)3012
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15833
89.4%
Uppercase Letter1869
 
10.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t3448
21.8%
i2329
14.7%
o2003
12.7%
e1548
9.8%
r1234
 
7.8%
s963
 
6.1%
m841
 
5.3%
p841
 
5.3%
a642
 
4.1%
c532
 
3.4%
Other values (5)1452
9.2%
Uppercase Letter
ValueCountFrequency (%)
C841
45.0%
D321
 
17.2%
A314
 
16.8%
P211
 
11.3%
O182
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Latin17702
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t3448
19.5%
i2329
13.2%
o2003
11.3%
e1548
8.7%
r1234
 
7.0%
s963
 
5.4%
C841
 
4.8%
m841
 
4.8%
p841
 
4.8%
a642
 
3.6%
Other values (10)3012
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17702
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t3448
19.5%
i2329
13.2%
o2003
11.3%
e1548
8.7%
r1234
 
7.0%
s963
 
5.4%
C841
 
4.8%
m841
 
4.8%
p841
 
4.8%
a642
 
3.6%
Other values (10)3012
17.0%

Churn Reason
Categorical

HIGH CORRELATION
MISSING

Distinct20
Distinct (%)1.1%
Missing5174
Missing (%)73.5%
Memory size55.1 KiB
Competitor had better devices
313 
Competitor made better offer
311 
Attitude of support person
220 
Don't know
130 
Competitor offered more data
117 
Other values (15)
778 

Length

Max length41
Median length32
Mean length25.25682183
Min length5

Characters and Unicode

Total characters47205
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor had better devices
2nd rowProduct dissatisfaction
3rd rowNetwork reliability
4th rowLimited range of services
5th rowCompetitor made better offer

Common Values

ValueCountFrequency (%)
Competitor had better devices313
 
4.4%
Competitor made better offer311
 
4.4%
Attitude of support person220
 
3.1%
Don't know130
 
1.8%
Competitor offered more data117
 
1.7%
Competitor offered higher download speeds100
 
1.4%
Attitude of service provider94
 
1.3%
Price too high78
 
1.1%
Product dissatisfaction77
 
1.1%
Network reliability72
 
1.0%
Other values (10)357
 
5.1%
(Missing)5174
73.5%

Length

2022-11-27T19:38:23.977135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitor841
 
12.6%
better624
 
9.4%
of453
 
6.8%
attitude314
 
4.7%
had313
 
4.7%
devices313
 
4.7%
made311
 
4.7%
offer311
 
4.7%
support263
 
4.0%
person220
 
3.3%
Other values (37)2694
40.5%

Most occurring characters

ValueCountFrequency (%)
e6138
13.0%
t5212
11.0%
4788
10.1%
o4650
 
9.9%
r3698
 
7.8%
i2918
 
6.2%
d2538
 
5.4%
s1917
 
4.1%
p1896
 
4.0%
a1816
 
3.8%
Other values (27)11634
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40330
85.4%
Space Separator4788
 
10.1%
Uppercase Letter1898
 
4.0%
Other Punctuation160
 
0.3%
Dash Punctuation29
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6138
15.2%
t5212
12.9%
o4650
11.5%
r3698
9.2%
i2918
 
7.2%
d2538
 
6.3%
s1917
 
4.8%
p1896
 
4.7%
a1816
 
4.5%
f1738
 
4.3%
Other values (13)7809
19.4%
Uppercase Letter
ValueCountFrequency (%)
C841
44.3%
A314
 
16.5%
P198
 
10.4%
L160
 
8.4%
D136
 
7.2%
N72
 
3.8%
S63
 
3.3%
M46
 
2.4%
E39
 
2.1%
W29
 
1.5%
Other Punctuation
ValueCountFrequency (%)
'130
81.2%
/30
 
18.8%
Space Separator
ValueCountFrequency (%)
4788
100.0%
Dash Punctuation
ValueCountFrequency (%)
-29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42228
89.5%
Common4977
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6138
14.5%
t5212
12.3%
o4650
11.0%
r3698
 
8.8%
i2918
 
6.9%
d2538
 
6.0%
s1917
 
4.5%
p1896
 
4.5%
a1816
 
4.3%
f1738
 
4.1%
Other values (23)9707
23.0%
Common
ValueCountFrequency (%)
4788
96.2%
'130
 
2.6%
/30
 
0.6%
-29
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII47205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6138
13.0%
t5212
11.0%
4788
10.1%
o4650
 
9.9%
r3698
 
7.8%
i2918
 
6.2%
d2538
 
5.4%
s1917
 
4.1%
p1896
 
4.0%
a1816
 
3.8%
Other values (27)11634
24.6%

Interactions

2022-11-27T19:38:19.133960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.171578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.061809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.787136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.547383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.279952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.948663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.609887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.315629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.023700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.688035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.368339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.030179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.683591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.417266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:19.780177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.252924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.112801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.840784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.594939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.325282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.992618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.656233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.364712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.068625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.733638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.412758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.075245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.729210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.467105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:19.823752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.344541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.158708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.891976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.640364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.368085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.034755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.702361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.415283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.110496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.777492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.454664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.117131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.772395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.515096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:19.870133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.411135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.210923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.945690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.689637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.413173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.079871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.749081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.463317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.156225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.829070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.501760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.161299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.818345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.564680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:19.913179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.481515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.258114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.994355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.732666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.456142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.121934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.791724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.509349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.200963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.874656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.547333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.202932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.863129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.611905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:19.958645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.536176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.305801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.044824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.782185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.500257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.165343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.837487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.555912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.246880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.920191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.593938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.245840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.908963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.658455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.003113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.585159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.352223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.091696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.834189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.544015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.208714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.882343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.601844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.292737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.965394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.637235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.288547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.956137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.706073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.049840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.639525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.401920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.142940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.882582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.591165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.254119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.929583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.652499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.338545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.012814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.683340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.334098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.009789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.759118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.094219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.688965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.454379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.191037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.930161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.636140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.298788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.975955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.699800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.382718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.058079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.726599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.377053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.065952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.806322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.139538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.735854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.499064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.238349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.976092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.678766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.341664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.019226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.744454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.424635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.101016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.768577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.419684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.114219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.851811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.186543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.787622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.545446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.290491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.027079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.724232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.387625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.065671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.791326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.469810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.146004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.813041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.464119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.163241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.900017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.231615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.835276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.587894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.337945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.075983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.766686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.429674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.110095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.834810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.511392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.187821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.854472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.505406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.215770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.944977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.277641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.881370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.635088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.388261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.124660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.809751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.472283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.155103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.878711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.553593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.230752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.896390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.548702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.266562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.991177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.326602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:09.945129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.686476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.444629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.180817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.857174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.518879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.207355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.927992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.599724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.276232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.941552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.594345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.317333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:19.039864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:20.375331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.009301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:10.738291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:11.497149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.234795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:12.903644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:13.565317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.262532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:14.976223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:15.645168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.323013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:16.986337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:17.639868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:18.368879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-27T19:38:19.087801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-27T19:38:24.023062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-27T19:38:24.112362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-27T19:38:24.199424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-27T19:38:24.290182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-27T19:38:24.399003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-27T19:38:20.489234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-27T19:38:20.864773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-27T19:38:21.035909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-27T19:38:21.165395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Customer IDGenderAgeMarriedNumber of DependentsCityZip CodeLatitudeLongitudeNumber of ReferralsTenure in MonthsOfferPhone ServiceAvg Monthly Long Distance ChargesMultiple LinesInternet ServiceInternet TypeAvg Monthly GB DownloadOnline SecurityOnline BackupDevice Protection PlanPremium Tech SupportStreaming TVStreaming MoviesStreaming MusicUnlimited DataContractPaperless BillingPayment MethodMonthly ChargeTotal ChargesTotal RefundsTotal Extra Data ChargesTotal Long Distance ChargesTotal RevenueCustomer StatusChurn CategoryChurn Reason
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40013-EXCHZFemale75Yes0Camarillo9301034.227846-119.07990333NoneYes7.38NoYesFiber Optic11.0NoNoNoYesYesNoNoYesMonth-to-MonthYesCredit Card83.90267.400.00022.14289.54ChurnedDissatisfactionNetwork reliability
50013-MHZWFFemale23No3Midpines9534537.581496-119.97276209Offer EYes16.77NoYesCable73.0NoNoNoYesYesYesYesYesMonth-to-MonthYesCredit Card69.40571.450.000150.93722.38StayedNaNNaN
60013-SMEOEFemale67Yes0Lompoc9343734.757477-120.550507171Offer AYes9.96NoYesFiber Optic14.0YesYesYesYesYesYesYesYesTwo YearYesBank Withdrawal109.707904.250.000707.168611.41StayedNaNNaN
70014-BMAQUMale52Yes0Napa9455838.489789-122.270110863Offer BYes12.96YesYesFiber Optic7.0YesNoNoYesNoNoNoNoTwo YearYesCredit Card84.655377.800.0020816.486214.28StayedNaNNaN
80015-UOCOJFemale68No0Simi Valley9306334.296813-118.68570307Offer EYes10.53NoYesDSL21.0YesNoNoNoNoNoNoYesTwo YearYesBank Withdrawal48.20340.350.00073.71414.06StayedNaNNaN
90016-QLJISFemale43Yes1Sheridan9568138.984756-121.345074365NoneYes28.46YesYesCable14.0YesYesYesYesYesYesYesYesTwo YearYesCredit Card90.455957.900.0001849.907807.80StayedNaNNaN

Last rows

Customer IDGenderAgeMarriedNumber of DependentsCityZip CodeLatitudeLongitudeNumber of ReferralsTenure in MonthsOfferPhone ServiceAvg Monthly Long Distance ChargesMultiple LinesInternet ServiceInternet TypeAvg Monthly GB DownloadOnline SecurityOnline BackupDevice Protection PlanPremium Tech SupportStreaming TVStreaming MoviesStreaming MusicUnlimited DataContractPaperless BillingPayment MethodMonthly ChargeTotal ChargesTotal RefundsTotal Extra Data ChargesTotal Long Distance ChargesTotal RevenueCustomer StatusChurn CategoryChurn Reason
70339975-SKRNRMale24No0Sierraville9612639.559709-120.34563901Offer EYes49.51NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMonth-to-MonthNoCredit Card18.9018.900.0049.5168.41JoinedNaNNaN
70349978-HYCINMale72Yes1Bakersfield9330135.383937-119.020428147NoneYes42.29NoYesFiber Optic22.0NoYesNoNoYesNoNoNoOne YearYesBank Withdrawal84.954018.050.0801987.636085.68StayedNaNNaN
70359979-RGMZTFemale20No0Los Angeles9002234.023810-118.15658207Offer EYes36.49NoYesFiber Optic42.0NoYesNoNoYesYesYesYesOne YearYesCredit Card94.05633.450.00255.43888.88StayedNaNNaN
70369985-MWVIXFemale53No0Hume9362836.807595-118.90154401Offer EYes42.09NoYesFiber Optic9.0NoNoNoNoNoNoNoYesMonth-to-MonthYesCredit Card70.1570.150.0042.09112.24ChurnedCompetitorCompetitor had better devices
70379986-BONCEFemale36No0Fallbrook9202833.362575-117.29964404NoneYes2.01NoNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNMonth-to-MonthNoBank Withdrawal20.9585.500.008.0493.54ChurnedCompetitorCompetitor made better offer
70389987-LUTYDFemale20No0La Mesa9194132.759327-116.997260013Offer DYes46.68NoYesDSL59.0YesNoNoYesNoNoYesYesOne YearNoCredit Card55.15742.900.00606.841349.74StayedNaNNaN
70399992-RRAMNMale40Yes0Riverbank9536737.734971-120.954271122Offer DYes16.20YesYesFiber Optic17.0NoNoNoNoNoYesYesYesMonth-to-MonthYesBank Withdrawal85.101873.700.00356.402230.10ChurnedDissatisfactionProduct dissatisfaction
70409992-UJOELMale22No0Elk9543239.108252-123.64512102Offer EYes18.62NoYesDSL51.0NoYesNoNoNoNoNoYesMonth-to-MonthYesCredit Card50.3092.750.0037.24129.99JoinedNaNNaN
70419993-LHIEBMale21Yes0Solana Beach9207533.001813-117.263628567Offer AYes2.12NoYesCable58.0YesNoYesYesNoYesYesYesTwo YearNoCredit Card67.854627.650.00142.044769.69StayedNaNNaN
70429995-HOTOHMale36Yes0Sierra City9612539.600599-120.636358163NoneNoNaNNaNYesCable5.0YesYesYesNoYesYesYesYesTwo YearNoBank Withdrawal59.003707.600.000.003707.60StayedNaNNaN